Discovering Contextual Information from User Reviews for Recommendation Purposes.
Konstantin Bauman, Alexander Tuzhilin · 2014
The paper presents a new method of discovering relevant contextual information from the user-generated reviews in order to provide better recommendations to the users when such reviews complement traditional ratings used in rec-ommender systems. In particular, we classify all the user reviews into the “context rich ” specific and “context poor” generic reviews and present a word-based and an LDA-based methods of extracting contextual information from the spe-cific reviews. We also show empirically on the Yelp data that, collectively, these two methods extract almost all the relevant contextual information across three di↵erent ap-plications and that they are complementary to each other: when one method misses certain contextual information, the other one extracts it from the reviews.